In [1]:
import os
from pathlib import Path
In [2]:
import pandas as pd
import numpy as np
In [3]:
import holoviews as hv
hv.extension('bokeh')
In [4]:
import plotly.offline as py
py.init_notebook_mode()
In [5]:
from dautil.plot import iplot_column_slider, plot_column_slider
In [6]:
%matplotlib inline
Power Spectra¶
In [7]:
def extract_z(path):
'''`path`: example: ps_z029.91_nf0.999799_useTs1_aveTb-03.79_200_300Mpc_v3
'''
return float(path.name.split('_')[1][1:])
In [8]:
def read_txt(path):
df = pd.read_csv(path, delim_whitespace=True, header=None, names=('k', 'P_k', 'std'))
df.set_index('k', inplace=True)
return df
In [9]:
def read_txts(paths):
dfs = (read_txt(path) for path in paths)
zs = [extract_z(path) for path in paths]
df = pd.concat(dfs, keys=zs)
df.index.names = ('z', 'k')
return df
In [10]:
%time df = read_txts(list(Path('~/21cmfast/21cmFAST/Out-of-box_Output/Power_spectra').expanduser().glob('*')))
In [11]:
%time df1 = read_txts(list(Path('~/21cmfast/21cmFAST/Output_files/Deldel_T_power_spec').expanduser().glob('*')))
In [12]:
def reshape_P_k(df):
return (df.P_k + 1.j * df['std']).unstack().T
In [13]:
P_k = reshape_P_k(df)
In [14]:
P_k1 = reshape_P_k(df1)
In [15]:
P_k_com = P_k.apply(np.real) + 1.j * P_k1.apply(np.real)
In [16]:
P_k.head()
Out[16]:
In [17]:
%%opts Curve {+framewise} [logx=True logy=True]
plot_column_slider(P_k1)
Out[17]:
In [18]:
%%opts Curve {+framewise}
# [logx=True logy=True]
plot_column_slider(P_k_com)
Out[18]:
In [19]:
py.iplot(iplot_column_slider(P_k1.apply(np.real)))
In [20]:
col_wo_na = P_k1.columns[~np.any(P_k1.isna(), axis=0)].values
In [21]:
col_wo_na.sort()
In [22]:
P_k1 = P_k1[col_wo_na]
Global Evolution¶
In [23]:
def read_global_evolution(path):
df = pd.read_csv(path, delim_whitespace=True, header=None, names=('zp', 'filling_factor_of_HI_zp', 'Tk_ave', 'x_e_ave', 'Ts_ave', 'T_cmb*(1+zp)', 'J_alpha_ave', 'xalpha_ave', 'Xheat_ave', 'Xion_ave'))
df.set_index('zp', inplace=True)
return df
In [24]:
path = Path('~/21cmfast/21cmFAST/Output_files/Ts_outs/global_evolution_Nsteps40_zprimestepfactor1.020_L_X3.2e+40_alphaX1.0_f_star100.0500_alpha_star0.5000_f_esc100.1000_alpha_esc-0.5000_Mturn5.0e+08_t_star0.5000_Pop2_200_300Mpc')
In [25]:
path = Path('~//21cmfast/21cmFAST/Output_files/Ts_outs/global_evolution_Nsteps40_zprimestepfactor1.020_L_X3.2e+40_alphaX1.0_f_star100.0500_alpha_star0.5000_f_esc100.1000_alpha_esc-0.5000_Mturn5.0e+08_t_star0.5000_Pop2_200_300Mpc')
In [26]:
!tail $path
In [27]:
df = read_global_evolution(path)
In [28]:
df.sort_index(inplace=True)
In [29]:
df.head()
Out[29]:
In [30]:
df.index.values
Out[30]:
In [56]:
df_big_z = df.loc[df.index >= 5.9]
# df_big_z = df
In [32]:
df.shape
Out[32]:
In [33]:
py.iplot(iplot_column_slider(df))
In [106]:
def format_float_array(array):
string = ','.join(f'{i:.16}' for i in array)
print(len(string))
return string
In [90]:
df_big_z.shape[0] + 3
Out[90]:
In [91]:
print(','.join(('0', '5.95', format_float_array(df_big_z.index.values), '30.')))
In [107]:
print(','.join(('-2', '-1', format_float_array(df_big_z.x_e_ave.values), '0')))
In [ ]: